Homemade Machine Learning Skill

基于星标22k+的开源项目打造，覆盖5种机器学习算法，核心功能包括算法原理（含数学推导）解释、获取交互式Jupyter Notebook链接、提取Python代码片段、生成分阶学习路径。用户通过bash命令调用子指令，输入算法名或学习难度即可获取对应内容，适用于机器学习入门、算法学习与代码参考场景。

bytesagain-lab

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最近更新 2026-05-11 17:44:36

分类：知识依赖：代码执行 / 联网浏览搜索应用场景：教学辅导 / 概念解释 / 研究 / 代码生成 ...

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add homemade-machine-learning-skill

Metadata

name: homemade-machine-learning-skill
description: Machine learning skill: find, explain, and implement ML algorithms with interactive Jupyter Notebook links. Covers linear regression, logistic regression, neural network, K-Means clustering, anomaly detection — with math, Python code, and skill demos.
version: 1.0.0
author: BytesAgain
homepage: https://bytesagain.com/skill/homemade-machine-learning-skill
tags: ["machine learning","jupyter notebook","python","data science","education","linear regression","neural network","skill"]

Homemade Machine Learning Skill

Machine learning skill: learn, explain, and implement ML algorithms from scratch. Based on trekhleb/homemade-machine-learning (MIT, 22k+ ⭐)

📦 Install: clawhub install homemade-machine-learning-skill

5 algorithms · 11 interactive notebooks · math explained · Python code included

Commands

explain — 解释算法原理 + 数学 + 代码

bash scripts/ml-notebook-finder.sh explain "linear regression"
bash scripts/ml-notebook-finder.sh explain "neural network"
bash scripts/ml-notebook-finder.sh explain "kmeans"

notebook — 获取交互式 Jupyter Notebook 链接

bash scripts/ml-notebook-finder.sh notebook "logistic regression"
bash scripts/ml-notebook-finder.sh notebook "anomaly detection"

code — 获取 Python 实现代码片段

bash scripts/ml-notebook-finder.sh code "linear regression"
bash scripts/ml-notebook-finder.sh code "kmeans"

path — 生成学习路径（按难度排序）

bash scripts/ml-notebook-finder.sh path beginner
bash scripts/ml-notebook-finder.sh path intermediate
bash scripts/ml-notebook-finder.sh path advanced

list — 列出所有算法

bash scripts/ml-notebook-finder.sh list

Algorithms

Algorithm	Type	Notebooks	Use Case
linear regression	supervised	3	price prediction, forecasting
logistic regression	supervised	4	classification, MNIST
neural network (MLP)	supervised	2	image recognition, deep learning
k-means	unsupervised	1	clustering, segmentation
anomaly detection	unsupervised	1	fraud detection, monitoring

Source

MIT License — Original author: trekhleb Indexed by BytesAgain — AI skill discovery platform